Thursday, 26th of January 2023, 12:00 – 1:00

Identifying image manipulations with analytical and learning-based methods

Venue: 
SR1

Lecturer:
Benedikt Lorch - researcher at SEC

Abstract: 

Verifying the authenticity of digital images is an important task in criminal investigations, journalistic fact-checking, and for insurance companies. To this end, a broad range of image forensics tools has been developed. These tools can broadly be categorized into analytical models and statistical learning. In the first part of this talk, I will present a model-based approach for image forensics with JPEG images. This approach exploits an artifact introduced by a popular JPEG library during chroma subsampling. The second part of the talk addresses one of the major challenges related to statistical learning. When machine learning tools are exposed to images that differ too much from the training data, they are prone to fail silently. We show how to mitigate silent failures using Bayesian detectors that can express uncertainty in their prediction.

 

 

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